Spark实战(六)spark SQL + hive(Python版)

一、hive环境准备

1、安装hive

   按照hive安装步骤安装好hive

CREATE USER 'spark'@'%' IDENTIFIED BY '123456';
GRANT all privileges ON hive.* TO 'spark'@'%';

flush privileges;

2、环境配置

   将配置好的hive-site.xml放入$SPARK-HOME/conf目录下,,下载mysql连接驱动，并拷贝至spark/lib目录下

   启动spark-shell时指定mysql连接驱动位置

bin/spark-shell --master spark://mini1:7077 --executor-memory 1g --total-executor-cores 2 --driver-class-path /home/hadoop/spark/lib/mysql-connector-java-5.1.35-bin.jar

二、Spark SQL

1、概述

   Spark SQL是Spark用来处理结构化数据的一个模块，它提供了一个编程抽象叫做DataFrame并且作为分布式SQL查询引擎的作用。

2、DataFrames

   与RDD类似，DataFrame也是一个分布式数据容器。然而DataFrame更像传统数据库的二维表格，除了数据以外，还记录数据的结构信息，即schema。同时，与Hive类似，DataFrame也支持嵌套数据类型（struct、array和map）。从API易用性的角度上 看，DataFrame API提供的是一套高层的关系操作，比函数式的RDD API要更加友好，门槛更低。由于与R和Pandas的DataFrame类似，Spark DataFrame很好地继承了传统单机数据分析的开发体验。

3、操作实例

from pyspark import Row
from pyspark.sql import SparkSession
from pyspark.sql.types import StructField, StringType, StructType

if __name__ == "__main__":
    spark = SparkSession\
            .builder\
            .appName("PythonWordCount")\
            .master("local")\
            .getOrCreate()
    sc = spark.sparkContext
    line = sc.textFile("D:\\code\\hadoop\\data\\spark\\day4\\person.txt").map(lambda x: x.split(' '))
    # personRdd = line.map(lambda p: Row(id=p[0], name=p[1], age=int(p[2])))
    # personRdd_tmp = spark.createDataFrame(personRdd)
    # personRdd_tmp.show()

    #读取数据
    schemaString = "id name age"
    fields = list(map(lambda fieldName: StructField(fieldName, StringType(), nullable=True), schemaString.split(" ")))
    schema = StructType(fields)

    rowRDD = line.map(lambda attributes: Row(attributes[0], attributes[1],attributes[2]))
    peopleDF = spark.createDataFrame(rowRDD, schema)
    peopleDF.createOrReplaceTempView("people")
    results = spark.sql("SELECT * FROM people")
    results.rdd.map(lambda attributes: "name: " + attributes[0] + "," + "age:" + attributes[1]).foreach(print)

    # SQL风格语法
    # personRdd_tmp.registerTempTable("person")
    # spark.sql("select * from person where age >= 20 order by age desc limit 2").show()
	#方法风格语法
    # personRdd_tmp.select("name").show()
    # personRdd_tmp.select(personRdd_tmp['name'], personRdd_tmp['age'] + 1).show()
    # personRdd_tmp.filter(personRdd_tmp['age'] > 21).show()
    # personRdd_tmp.groupBy("age").count().show()

    
    # personRdd_tmp.createOrReplaceTempView("people")
    # sqlDF = spark.sql("SELECT * FROM people")
    # sqlDF.show()

    # personRdd_tmp.createGlobalTempView("people")
    # spark.sql("SELECT * FROM global_temp.people").show()
    #
    # spark.newSession().sql("SELECT * FROM global_temp.people").show()

	# 保存为指定格式
    # people = line.map(lambda p: (p[0],p[1], p[2].strip()))
    # schemaString = "id name age"
    #
    # fields = [StructField(field_name, StringType(), True) for field_name in schemaString.split()]
    # # # 通过StructType直接指定每个字段的schema
    # schema = StructType(fields)
    # schemaPeople = spark.createDataFrame(people, schema)
    # schemaPeople.createOrReplaceTempView("people")
    # results = spark.sql("SELECT * FROM people")
    # results.write.json("D:\\code\\hadoop\\data\\spark\\day4\\personout.txt")
    # results.write.save("D:\\code\\hadoop\\data\\spark\\day4\\personout1")

    # results.show()

   结果如下：

   保存为JSON文件如下

   从mysql中读写数据

	#JDBC
    # sqlContext = SQLContext(sc)
    # df = sqlContext.read.format("jdbc").options(url="jdbc:mysql://localhost:3306/hellospark",
    #                                             driver="com.mysql.jdbc.Driver", dbtable="(select * from actor) tmp",
    #                                             user="root", password="123456").load()

    # schemaPeople.write \
    #     .format("jdbc") \
    #     .option("url", "jdbc:mysql://localhost:3306/hellospark")\
    #     .option("dbtable", "person")\
    #     .option("user", "root")\
    #     .option("password", "123456")\
    #     .mode("append")\
    #     .save()

三、Spark Sql + Hive

   需要指定warehouse_location，并将配置好的hive-site.xml放入$SPARK-HOME/conf目录下

from os.path import abspath	
from pyspark.sql import SparkSession
# from pyspark.sql.types import StructField, StringType, StructType

if __name__ == "__main__":
    # spark = SparkSession\
    #         .builder\
    #         .appName("PythonWordCount")\
    #         .master("spark://mini1:7077")\
    #         .getOrCreate()
    # sc = spark.sparkContext
    warehouse_location = abspath('spark-warehouse')

    spark = SparkSession \
        .builder \
        .appName("Python Spark SQL Hive integration example") \
        .config("spark.sql.warehouse.dir", warehouse_location) \
        .enableHiveSupport() \
        .getOrCreate()

    # spark.sql("CREATE TABLE IF NOT EXISTS person(id STRING, name STRING,age STRING) row format delimited fields terminated by ' '")
    # spark.sql("LOAD DATA INPATH 'hdfs://mini1:9000/person/data/person.txt' INTO TABLE person")
    spark.sql("SELECT * FROM person").show()

    # line = sc.textFile("hdfs://mini1:9000/person/data/person.txt").map(lambda x: x.split(' '))
    # people = line.map(lambda p: (p[0],p[1], p[2].strip()))
    # schemaString = "id name age"
    #
    # fields = [StructField(field_name, StringType(), True) for field_name in schemaString.split()]
    # # # 通过StructType直接指定每个字段的schema
    # schema = StructType(fields)
    # schemaPeople = spark.createDataFrame(people, schema)
    # schemaPeople.createOrReplaceTempView("people")
    #
    # schemaPeople.write \
    #     .format("jdbc") \
    #     .option("url", "jdbc:mysql://192.168.62.132:3306/hellospark")\
    #     .option("dbtable", "person")\
    #     .option("user", "root")\
    #     .option("password", "123456you")\
    #     .mode("append")\
    #     .save()

    spark.stop()
    # sc.stop()

   还可以使用org.apache.spark.sql.hive.HiveContext来实现。